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A New Model and Method for Order Selection Problems in Flow-Shop Production

In: Optimization and Control for Systems in the Big-Data Era

Author

Listed:
  • Jun Wang

    (Qingdao University)

  • Xiaoxia Zhuang

    (Qingdao University)

  • Baiyi Wu

    (Guangdong University of Foreign Studies)

Abstract

As the economic growth of China gradually slows down in recent years, the flow-shop production enterprises pay more and more attention to the production capacity planning problem. The order selection problem plays a central role in the production capacity planning of flow-shop production enterprises. Traditional order selection models separate the processes of production scheduling and order selection. The performance of the order selection depends entirely on production scheduling. In this paper we study the relationship between the processes of order selection and production scheduling, and propose a new nonlinear 0–1 programming model aiming at profit maximization. Our new model considers simultaneously order selection and production scheduling and we will demonstrate that our new model generates a production schedule that is much better than that from traditional models. We solved the new model using Lingo 11.0 and numerical results show that the optimal solution can be obtained within an hour on a personal computer when the order size is less than 16.

Suggested Citation

  • Jun Wang & Xiaoxia Zhuang & Baiyi Wu, 2017. "A New Model and Method for Order Selection Problems in Flow-Shop Production," International Series in Operations Research & Management Science, in: Tsan-Ming Choi & Jianjun Gao & James H. Lambert & Chi-Kong Ng & Jun Wang (ed.), Optimization and Control for Systems in the Big-Data Era, chapter 0, pages 245-251, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-53518-0_13
    DOI: 10.1007/978-3-319-53518-0_13
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    Cited by:

    1. Mohamadreza Dabiri & Mehdi Yazdani & Bahman Naderi & Hassan Haleh, 2022. "Modeling and solution methods for hybrid flow shop scheduling problem with job rejection," Operational Research, Springer, vol. 22(3), pages 2721-2765, July.

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